Chinese Ideograms as Intermediaries to Reading

Chinese characters contain three levels of visual information: strokes, radicals, and structures. Stroke, as the basic unit of Chinese characters, is the foundation of the formation of Chinese characters. Some research has shown that strokes can enhance the semantic and orthographic representation in the process of Chinese character acquisition. This shows that the processing of Chinese characters is based on feature processing. This is consistent with the feature processing theory of letter and object recognition. Recent research on visual recognition shows that the recognition of letters and words is based on the termination and the other features (vertices and midsegment) used in object recognition. In this study, we used delayed segment technique on Chinese character recognition to examine the importance of terminations, vertices and midsegment in different masking degree. Experiment 1 selected about 43 high frequency Chinese characters with left-right structures and asked the participants to naming Chinese characters. In experiment 1, we try to examine the importance of terminations, vertices and midsegments based on line unit and consider the degree of masking these features. It was found that the main effect of masking degree was significant. The masking of 35% and 55% of the features makes the RTs significantly longer. The main effect of feature type was significant. Masking vertices and midsegment can make RTs significantly longer. But termination didn’t show its feature effect in experiment 1. When masking these three features (vertices, midsegment and termination) in experiment 1, the features are based on line units, which may destroy the integrity of strokes in Chinese characters. That termination effects couldn’t be obvious in experiment 1 may be caused by this reason. To verify this, in experiment 2, we explore the vertices, midsegment and termination based on stroke units. The results show that the main effect of masking degree was significant, and the RTs were longer by masking 35% and 55% degree. The main effect of feature types was significant. RTs on character recognition are significantly longer by masking the vertices, midsegment and termination. This shows that the vertices, midsegment and termination based on stroke unit are also important in Chinese character recognition. Based on the above research results, it can be seen that the key features on Chinese character recognition are in line with the neuronal recycling hypothesis. According to neuronal recycling hypothesis, human’s ability to learn from culture depends on the retrieval of preexisting brain circuits, and the key features in word recognition may be derived from our key features in object processing. The hook is considered to be a special termination in Chinese characters. The results of experiment 2 also emphasized the key role of hook features in character recognition.

Non-negative matrix factorization (NMF) was introduced by Paatero and Tapper in 1994 and it was a general way of reducing the dimension of the matrix with non-negative entries. Non-negative matrix factorization is very useful in many data analysis applications such as character recognition, text mining, and others. This paper aims to study the application in Chinese character recognition using non-negative matrix factorization. Python was used to carry out the LU factorization and non-negative matrix factorization of a Chinese character in Boolean Matrix. Preliminary analysis confirmed that the data size of and and are chosen for the NMF of the Boolean matrix. In this project, one hundred printed Chinese characters were selected, and all the Chinese characters can be categorized into ten categories according to the number of strokes , for . The Euclidean distance between the Boolean matrix of a Chinese character and the matrix after both LU factorization and NMF is calculated for further analysis. Paired t-test confirmed that the factorization of Chinese characters in the Boolean matrix using NMF is better than the LU factorization. Finally, ten handwritten Chinese characters were selected to test whether the program is able to identify the handwritten and the printed Chinese characters. Experimental results showed that 70% of the characters can be recognized via the least Euclidean distance obtained. NMF is suitable to be applied in Chinese character recognition since it can reduce the dimension of the image and the error between the original Boolean matrix and after NMF is less than 5%.

Chinese Optical Character Recognition (OCR) technology is essential for digital transformation in Chinese regions, enabling automated document processing across various applications. However, Chinese OCR systems struggle with visually similar characters, where subtle stroke differences lead to systematic recognition errors that limit practical deployment accuracy. This study develops FLIP (Feedback Learning-based Intelligent Plugin), a lightweight post-processing plugin designed to improve Chinese OCR accuracy across different systems without external dependencies. The plugin operates through three core components as follows: UTF-8 encoding-based output parsing that converts OCR results into mathematical representations, error correction using information entropy and weighted similarity measures to identify and fix character-level errors, and adaptive feedback learning that optimizes parameters through user interactions. The approach functions entirely through mathematical calculations at the character encoding level, ensuring universal compatibility with existing OCR systems while effectively handling complex Chinese character similarities. The plugin’s modular design enables seamless integration without requiring modifications to existing OCR algorithms, while its feedback mechanism adapts to domain-specific terminology and user preferences. Experimental evaluation on 10,000 Chinese document images using four state-of-the-art OCR models demonstrates consistent improvements across all tested systems, with precision gains ranging from 1.17% to 10.37% and overall Chinese character recognition accuracy exceeding 98%. The best performing model achieved 99.42% precision, with ablation studies confirming that feedback learning contributes additional improvements from 0.45% to 4.66% across different OCR architectures.

This study designed a finger-worn device-named the Chinese FingerReader-that can be practically applied by visually impaired users for recognizing traditional Chinese characters on the micro internet of things (IoT) processor. The device is portable, easy to operate, and designed to be worn on the index finger. The Chinese FingerReader on the index finger contains a small camera and buttons. The small camera captures images by identifying the relative position of the index finger to the printed text, and the buttons are applied to capture an image by visually impaired users and provide the audio output of the corresponding Chinese character by a voice prompt. To recognize Chinese characters, English letters, and numbers, a robust Chinese optical character recognition (OCR) system was developed according to the training strategy of an augmented convolution neural network algorithm. The proposed Chinese OCR system can segment a single character from the captured image, and the system can accurately recognize rotated Chinese characters. The experimental results revealed that compared with the OCR application programming interfaces of Google and Microsoft, the proposed OCR system obtains 95% accuracy rate in dealing with rotated character images where the Google and Microsoft OCR APIs only obtain 65% and 34% accuracy rates. These results illustrate that the proposed OCR system was more suitable for the needs of visually impaired people in actual use. Finally, three usage scenarios were simulated, and the accuracy and operational performance of the system were tested. Field tests of this system were conducted for visually impaired users to verify its feasibility.

Scene character recognition problem has attracted a great attention in computer vision field. As one of the most widely used characters, Chinese characters are more complicated than European characters, especially when it comes to those characters appeared in various scene texts. The recognition of Chinese scene characters is still far from a satisfactory level owing to the lack of adequate training data and efficient learning methods. In this paper, we propose a new strategy to divide training data and combine multiple classifiers to get a better performance of Chinese scene character recognition. Besides, facing the problem of data shortage, we propose a scene character synthesis method to gain enough training data. By applying the proposed training strategy, the average recognition accuracies of Random Forest (RF) and Support Vector Machine (SVM) have been improved by nearly 22% and 14%, respectively.

In this study, we investigated the role of visual and phonological information in lexical access of Chinese characters. Homophonic English words have been the main source of stimuli for word recognition research. However, since these stimuli also often look alike, visual and phonological information may be confounded in reported experiments. In contrast, many homophonic Chinese characters are visually distinct. In addition, visually similar characters often have very different pronunciations. These characteristics allow a more controlled investigation of the roles of visual and phonological information in activation of meaning. In the present study, two types of Chinese characters were used in a semantic categorization paradigm: integrated characters, which contain strokes that are not separable; and compound characters, which contain at least two clearly identifiable components. The results show that the recognition of a Chinese integrated character depends primarily on visual information, whereas the recognition of a Chinese compound character relies on visual, phonological, and semantic information. It is concluded that visual information plays a greater role in Chinese character recognition than has previously been documented.

This paper applies deep siamese network to one-shot Chinese handwritten character recognition. Different from common image classification tasks, the CASIA HWDB1.1 dataset used here contains more than 3000 categories, with only few samples in each one. We propose a basic deep siamese model as well as an improved model with multi-layer features mechanism and batch normalization for extracting the similarity of the input pairs, and implement one-shot recognition by categorizing the test example to the class where the support sample is the most similar. Experiments prove that our model is able to recognize Chinese characters of unseen classes in training with only one support example efficiently.

In this paper we study the problem of Chinese character recognition in video. We propose a series of algorithms on Chinese character division, tracking. Based on them we design a multi-level sorter. Firstly we extract the features of some samples and employ K-means clustering algorithm to carry on I level classification. Secondly, we employ the algorithm of multi back propagation neural network (MBPNN) to classify every category once again and we call it II level classification. Finally, we carry on the experiment and the testing result proves that these algorithms are effectively and recognition rate is higher than conventional back propagation neural network.

The present study adopts the priming word-judging paradigm to investigate whether the critical period will influence radical and lexical tone awareness during the bilinguals' Chinese character recognition. Two types of bilinguals (simultaneous and sequential bilinguals) will be recruited as participants in the present study. The different features between these two types of participants are used to test the influence of the critical period. Through a Chinese character decision task experiment, we hypothesis that the critical period will influence the semantic radical and lexical tone awareness while processing the Chinese characters of bilinguals. In addition, this study will provide some advice to learning Chinese characters in second language acquisition.

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.

Recent studies have shown that young children’s orientation sensitivity is correlated with their reading ability. However, the development of orientation sensitivity to Chinese characters with different complexities (e.g., simple characters and compound characters) in preschool children is unknown. To fill this gap, the current three-year longitudinal study aimed to investigate the development of orientation sensitivity to Chinese simple characters and compound characters. A total of 50 Chinese children were first tested at their first kindergarten years (T1: mean age = 3.62 years) and then tested annually on a variety of tasks for three years (T2: mean age = 4.63 years; T3: mean age = 5.58 years). Children’s orientation sensitivity to Chinese characters was assessed using two perceptual-matching tasks. The results showed that the inverted and mirrored orientation sensitivity to Chinese characters demonstrated different developmental trajectories. Specifically, children possessed inverted and mirrored orientation knowledge of Chinese simple characters at the age of 4. They also displayed sensitivity to inverted Chinese compound characters at the age of 5. However, mirrored orientation knowledge of Chinese compound characters did not emerge until the age of 5. These results suggest that children’s inverted orientation knowledge of Chinese characters developed earlier. Moreover, the complexity of Chinese characters affected the development of orientation knowledge of Chinese characters.

A VLSI implementation of the optical Chinese character recognition (OCCR) system with pipelined and parallel structure is presented. We also propose an efficient method for performing block classification and character segmentation as well as an effective and adaptive feature extraction algorithm for recognizing multifont printed Chinese characters. With the complex and huge amount of data involved in Chinese characters, their recognition requires numerous complex computations. Therefore, to improve the recognition efficiency for practical applications, a VLSI chip is designed and fabricated. To preserve a certain degree of flexibility so that various recognition algorithms can be implemented with the system, only the most time-consumig parts are implemented into the VLSI circuit. By combining the VLSI technology and the effective Chinese character recognition algorithm, a practical OCCR system with high speed, high-recognition rate, and accumulated learning capability is developed. Based on the experimental results, the VLSI chip can process up to 200 characters/s, which is one hundred times faster than the original software algorithm. The recognition rates of three different test conditions are also given.

This paper proposes an efficient technique of shape feature extraction based on the application of mathematical morphology theory. A new shape complexity index for preclassification of machine printed Chinese Character Recognition (CCR) is also proposed. For characters represented in different fonts/sizes or in a low resolution environment, a more stable local feature such as shape structure is preferred for character recognition. Morphological valley extraction filters are applied to extract the protrusive strokes from four sides of an input Chinese character. The number of extracted local strokes reflects the shape complexity of each side. These shape features of characters are encoded as corresponding shape complexity indices. Based on the shape complexity index, data base is able to be classified into 16 groups prior to recognition procedures. The performance of associating with shape feature analysis reclaims several characters from misrecognized character sets and results in an average of 3.3% improvement of recognition rate from an existing recognition system. In addition to enhance the recognition performance, the extracted stroke information can be further analyzed and classified its own stroke type. Therefore, the combination of extracted strokes from each side provides a means for data base clustering based on radical or subword components. It is one of the best solutions for recognizing high complexity characters such as Chinese characters which are divided into more than 200 different categories and consist more than 13,000 characters.

Stroke extraction of Chinese characters plays an important role in Chinese character recognition, calligraphy aesthetic quality evaluation and handwriting synthesis. However, most of the existing methods perform well on printed characters, but not well on ancient calligraphy and handwritten characters. In addition, most of these methods only use a single model based on graph theory or deep learning to achieve end-to-end stroke extraction, resulting in limited room for model performance improvement. In this paper, we propose a Chinese character stroke extraction method based on hierarchical multi-model including four steps, which apply KNN clustering algorithm into stroke extraction and improved the performance of stroke extraction based on instance segmentation.Firstly, we use a stacked model driven calligraphic character recognition method to recognize the input character image. Then we use the font recognition model to match the input character image with images with annotated strokes. For those images with matching rate higher than 99\%, stroke extraction method based on KNN pixel clustering is used to extract the strokes. Thirdly, for those images with matching rate less than 99\%, we separate the non-connected regions of the input character image according to the graphic attributes to find out the independent strokes. Finally, the instance segmentation-based stroke extraction method is applied to extract each individual stroke. Experimental results show that the average accuracy of the proposed method on the five test subsets of CSSCD, LTH, SS, HLJ, FZJTJW and FZLBJW is 94.3\%, 96.2\%, 91.7\%, 92.9\% and 89.4\%, respectively, which is greatly improved compared with the existing methods, which proves the effectiveness of the method described in this paper. Our code and datasets are publicly available at \url{https://github.com/JacksonMa618/MLStroke}

Research has shown that memorizing a character by its strokes is perhaps the most difficult aspect of learning the Chinese language, particularly for beginning students. Since learners can handwrite Chinese characters without knowing the meaning or the pronunciation, emphasizing drawing the characters from the beginning is not an efficient way of learning Chinese characters. Meanwhile, character recognition, grammar pattern drills, and memorization are not enough for college Chinese language learning. Currently, there is a gap in the literature about Chinese language acquisition research from classroom contexts to naturalistic settings, from an acquisition metaphor to a participation metaphor, and from Chinese language to Chinese language use. The goal of this study was to investigate the learners’ perceptions about Computer Assisted Chinese Character Learning (CACCL) in a proficiency oriented professionally focused program in the U.S. and to examine if the CACCL works for Chinese language learners in the program. When the data is followed by in-depth exploratory surveys, learning sessions with both CACCL and non-CACCL, read-aloud assessments, typing assessments, and handwriting assessments with CACCL and non- CACCL, this study focuses on Chinese learners whose proficiency is both at the novice and intermediate levels. Findings from this study indicated that one possible approach may involve the use of bridging the pronunciation and the character so that digital tools can offer support and improvement of existing Chinese teaching instructions. Quality Chinese language education may fundamentally depend on language learning interaction but also computer assisted language learning curriculum design. My data also suggest that there is a need to understand how students can best apply self-regulated learning strategies to achieve academic success in a virtual environment so that they can be more effective with the provided online resources as virtual Chinese language learning continues to increase.